Background: Both immunogenic cell death (ICD) and long noncoding RNAs (lncRNAs) are strongly associated with tumor development, but the mechanism of action of ICD-associated lncRNAs in hepatocellular carcinoma (HCC) remains unclear.Methods: We collected data from 365 HCC patients from The Cancer Genome Atlas (TCGA) database. We formulated a prognostic signature of ICD-associated lncRNAs and a nomogram to predict prognosis. To explore the potential mechanisms and provide clinical guidance, survival analysis, enrichment analysis, tumor microenvironment analysis, tumor mutation burden (TMB), and drug sensitivity prediction were conducted based on the subgroups obtained from the risk score.P=0.004). Chemotherapy drug sensitivity prediction provides effective guidelines for individual therapy. RT-qPCR of human HCC tissues verified the accuracy of the model.Results: A prognostic signature of seven ICD-associated lncRNAs was constructed. Kaplan–Meier (K-M) survival curves showed a more unfavorable outcome in high-risk patients. The nomogram had a higher predictive value than the nomogram constructed without the risk model. Enrichment analysis confirmed that risk lncRNAs were closely associated with cell proliferation and mitosis. Most of the immune checkpoints currently used in therapy  appeared to be elevated in high-risk patients. Tumor microenvironment analysis showed differential expression of lymphocytes  in the high-risk group. TMB had a higher incidence of mutations in the high-risk group (Conclusion: We constructed an effective prognostic signature for patients with HCC using seven ICD-lncRNAs, which provides guidance for the prognostic assessment and personalized treatment of patients. Hepatocellular carcinoma is the sixth most prevalent primary carcinoma and the third most common cause of cancer-related death globally. Hepatocellular carcinoma (HCC) constitutes 90% of liver cancer cases and is one of the most prevalent malignancies with rapid progression and deterioration and an adverse prognosis, with a 5-year survival rate of <50% . Tumor nImmunogenic cell death (ICD), a form of apoptosis mediated by activation of immune cells through specific antigen recognition , is closhttps://portal.gdc.cancer.gov/, accessed on October 30, 2022) for patients with HCC, yielding a series of 424 specimens, including 374 tumor tissue specimens and 50 normal tissue specimens. The data were processed as shown in We obtained transcriptomic gene expression information, single-nucleotide mutation information, survival information, and clinical phenotype data from The Cancer Genome Atlas (TCGA) database , according to a score>35’ [R|2>0.3, P<0.001) was performed by the corr.test function in R (4.2.1).A total of 139 ICD-associated genes were obtained from Genecards (core>35’ . Pearson2Fold Change|>1 and P<0.05) [To identify differentially expressed genes between tumor specimens and normal specimens, we used the ‘DESeq2’ package in R (4.2.1) to conduct differential analysis of lncRNAs obtained from Pearson correlation analysis (|log P<0.05) .Univariate Cox, LASSO regression, and multivariate Cox analyses were performed sequentially in the training cohort by means of the ‘survival’ package in R (4.2.1) . The folFor the prognostic signature, we performed survival analysis for the training cohort, test cohort, and entire cohort using the ‘survival’ package in R (4.2.1). Time-dependent ROC curves were plotted by using the ‘rms’ package and time-ROC package .To validate the prognostic signature in relation to other clinical information, we used the ‘survival’ package for independent prognostic analysis and the ‘ggDCA’ package, the ‘rms’ package, and the ’foreign’ package for DCA .In accordance with the different clinical characteristics, we calculated the risk scores in different clinical subgroups and grouped them for survival analysis to visualize the applicability of our risk model.To yield a more comprehensive and accurate risk prediction model, we combined the clinical information of HCC patients and plotted a nomogram and calibration curves using the ‘survival’ package, ‘regplot’ package, and ‘rms’ package .To better visualize the divergent genetic performance among high- and low-risk patients, we performed PCA of complete patient genes, ICD-associated genes, ICD-lncRNA genes, and risk-ICD-lncRNA genes using the ‘scatterplot3d’ package in R .q-value < 0.01.We performed pathway enrichment by GSEA (4.3.2) using the groupings obtained by risk score; specifically, the datasets used were biocarta, kegg, pid, reactome, and go in MSigDB . The pathttp://timer.cistrome.org/, accessed on November 4, 2022) [We performed immune infiltration analysis of a full set of HCC patients using the CIBERSORT algorithm in R (4.2.1). By using the ‘GSVA’, ‘GSWABase’, and ‘limma’ packages, we performed immune function analysis in both patient groups. We also performed immune scoring using different algorithms  through the timer2.0 website , we generated waterfall plots of mutation proportions and types and performed analytical calculations of tumor mutation compliance for each of the two patient subgroups according to risk score .https://www.cancerrxgene.org/, accessed on November 4, 2022) and used the ‘oncoPredict’ package to compare the IC50 differences among 198 chemotherapeutic drugs in combination with information from HCC patients [We downloaded information on the effectiveness of chemotherapy from the GDSC website . BlazeTaq SYBR Green qPCR master mix  and Applied Biosystems 7500 Fast Real-Time PCR System (Applied Biosystems) were used for subsequent RT-qPCR analysis. The relevant primers used are shown in Supplementary Table S1. The expression of each gene is expressed as the 2https://www.proteinatlas.org/, accessed on January 25, 2023).To verify the relevance of ICD-associated genes to HCC, we compared the differential expression of ICD-associated genes between HCC and normal tissues through the Human Protein Atlas database . LASSO regression was used to further screen the relevant lncRNAs  and low-risk groups (n=183). The clinical information of patients in each group is shown in The multivariate Cox analysis results were combined using the following formula: risk score = (1.576275843 × LINC01136 expression) + (−3.014558831 × RP11.298O21.2 expression) + (0.911419424 × RP11.20J15.2 expression) + (1.50737826 × LINC00501 expression) + (4.267512039 × UBE2E1.AS1 expression) + (4.066672785 × LINC01060 expression) + (3.247792567 × RP5.828H9.1 expression). Using this formula, we assigned risk scores to all HCC patients, and we categorized the patients into high-  and multivariate Cox regression analysis  indicated that risk score is an individual prognostic factor (Supplementary Table S4). The results of the correlation between the risk score and the three clinical factors indicated that age and sex were not substantially correlated with the risk score, but clinical stage was correlated .PCA was carried out separately for all genes, ICD-associated genes, ICD-associated lncRNAs, and risk-ICD-lncRNAs. 3dPCA plots showed that the seven prognosis-associated lncRNAs were most strikingly different between the high- and low-risk groups .We plotted ROC curves for the four factors A, and thIn an attempt to yield a more comprehensive and accurate prognostic score, we integrated the patients’ clinical information with the risk score to derive a nomogram B to predIn the Biocarta enrichment analysis, the low-risk gene set was functionally associated with endogenous thrombospondin activation, lipid metabolism and toxic nuclear receptors, and complement pathways; the high-risk genes were relevant to cell cycle regulation, BRCA1/2, and ATR susceptibility in cancer A. In theIn the immunoinfiltration analysis, antigen-presenting cells (APCs), macrophages, follicular regulatory T lymphocytes (Tfhs), T helper 2 cells (Th2), and regulatory T lymphocytes (Tregs) were highly frequent in the high-risk group; B cells and natural killer (NK) cells were highly expressed in the low-risk group , as detailed in Supplementary Figure S6A–J.In the immune function analysis, APC_co_stimulation, HLA, and MHC_class_I scores were higher in the high-risk group, while Type_II_IFN_Response scores were higher in the low-risk group .The immune differential analysis by seven different algorithms is shown in the significance bubble plot, which had similar results to those of ssGSEA, indicating that immune cells tend to infiltrate more in high-risk patients .P-value = 0.004  in the high-risk group , implying that it may be possible that the high-risk group may benefit more from immune-associated therapies against these checkpoints.By comparing the IC50 of anticancer drugs across high- and low-risk patients, we yielded an aggregate table of 91 drugs that could be used in clinical treatment (Supplementary Table S7). High-risk patients were more sensitive to drugs related to mitosis, the cytoskeleton, protein stability destruction and part of the cell cycle , while low-risk patients were more sensitive to DNA replication-related drugs . The remaining 107 drugs with no difference between the high- and low-risk groups are displayed in Supplementary Table S8, indicating that the model cannot determine the group preference of these drugs. A summary of the effective drugs and their differences is shown in Supplementary Table S9.We derived the protein expression levels of five of the ICD-associated genes in HCC and normal tissues from the HPA database A.The expression of seven ICD-associated risk lncRNAs was validated in RT-qPCR experiments in high- and low-risk patients. The results demonstrated that all seven lncRNAs were differentially expressed B, and alHCC is a global health concern, with the incidence reaching >90,000 cases by 2025 . ICD is P<0.0001 in TCGA, P=0.013 in GSE41613) [P<0.01 in TCGA, P=0.009 in GEO) and GBMLGG  [P<0.001 in the training cohort, P=0.029 in the testing cohort), the prognosis was worse in the high-risk group. In these studies, we noticed that immune recognition sites and killer immune cells were less abundant in the high-risk group. This led to the assumption that ICD was an important process of self-protection against tumors. Collectively, the ICD-associated prognostic signature was a worthy method for evaluating prognosis.Previous studies have shown that ICD is closely related to tumor development . Hence, SE41613) . The samin CGGA) ,11. ConsThe progression of HCC is strongly related to the dysregulation of the host's immune mechanisms. Alessandro Granito et al. described that Tregs, with CD4+ CD25+ Foxp3+ as molecular markers, had a suppressive effect on CD8+ T cells and NK cells in the development of HCC, thus reducing the effect of the host immune system on tumor cell death . ConsistThe GSEA results indicated that the malignancy of HCC in the high-risk group was related not only to the TME but also to HCC cells. Lu’s study illustrated that c-MYC and cyclin D1 were both targets of the WNT pathway, and up-regulation of β-catenin produced by the WNT pathway led to up-regulation of both targets . In addiThe results of the drug sensitivity prediction analysis provided guidance for individualized treatment after evaluation. Immunotherapy and chemotherapeutic agents are two of the commonly used options for HCC . Sagar eOur study established a novel prognostic signature of ICD in combination with lncRNAs in HCC. In the present study cohort, the predictive performance of TMN staging was inferior to that of the obtained prognostic model. Wu et al. established a prognostic signature for ECM-related lncRNA HCC with AUCs of 0.746, 0.683, and 0.670 for time-ROC curves at 1, 3, and 5 years in their TCGA cohort . In a seWe constructed an effective prognostic signature for patients with HCC using seven ICD-lncRNAs, which provided guidance for the prognostic assessment and personalized treatment of patients. In addition, ICD was integrated with WNT pathway-induced immune escape and malignant tumor biological behavior for the analysis of potential mechanisms. To this end, we also provide guidance on clinical agents  for the treatment of patients after evaluation. Due to the limitations of experiments and specimens, we need more basic, mechanistic and clinical studies for validation.Click here for additional data file.


To investigate whether ivermectin inhibits SARS-CoV-2 proliferation in patients with mild-to-moderate COVID-19 using time to a negative COVID-19 reverse transcription-polymerase chain reaction (RT-PCR) test.CORVETTE-01 was a double-blind, randomized, placebo-controlled study (August 2020–October 2021) conducted in Japan. Overall, 248 patients diagnosed with COVID-19 using RT-PCR were assessed for eligibility. A single oral dose of ivermectin (200  μg/kg) or placebo was administered under fasting. The primary outcome was time to a negative COVID-19 RT-PCR test result for SARS-CoV-2 nucleic acid, assessed using stratified log-rank test and Cox regression models.p = 0.785). Median (95% CI) time to a negative RT-PCR test was 14.0 (13.0–16.0) and 14.0 (12.0–16.0) days for ivermectin and placebo, respectively; 82.1% and 84% of patients achieved negative RT-PCR tests, respectively.Overall, 112 and 109 patients were randomized to ivermectin and placebo, respectively; 106 patients from each group were included in the full analysis set . No significant difference was observed in the occurrence of negative RT-PCR tests between the groups (hazard ratio, 0.96; 95% confidence interval [CI] 0.70–1.32; In patients with COVID-19, single-dose ivermectin was ineffective in decreasing the time to a negative RT-PCR test.ClinicalTrials.gov, NCT04703205. Drug rein vitro study demonstrated that SARS-CoV-2 viral RNA was reduced with ivermectin 5 μM versus control . Eligible patients were randomized 1:1 to ivermectin or placebo  guidelines for RCTs were followed for reporting the study .2.2.2) of ≥95%, weighed ≥40 kg at eligibility assessment, and could provide written informed consent were included in the study. Patients who received prohibited concomitant medications within the past month (past 6 months for biologics) or required prohibited concomitant medications (see Patients aged ≥20 years who were diagnosed with COVID-19 (including asymptomatic cases) using an RT-PCR test within 3 days before eligibility assessment, reported room air oxygen saturation  at enrollment; on days 3, 5, 7, 9, 11, 13, and 15. Patients who did not test negative for SARS-CoV-2 on RT-PCR at the protocol-defined test visits were continued to be tested until a negative result occurred at bidaily intervals for up to a maximum of 45 days.2 in room air (SpO2 ≥95% lasting 24 h) when oxygen support was required; and the proportion of patients whose clinical status improved from enrollment by more than one stage from stage 3 of the 7-point ordinal scale on day 15.Secondary outcomes included the proportion of patients with a change in disease status between enrollment and day 15 see , with onAEs including subjective and objective findings at presentation and change in vital signs  and AEs observed in laboratory parameters were assessed. Subjective findings were collected using an electronic device see .2.5.The target sample size was set at 240 patients to achieve a significance level of 5% and a power of ≥80% in a two-sided log-rank test and considering that a few patients would be unevaluable. It was estimated that 192 events would be required among 210 patients in the two treatment groups over an observation period of 45 days, assuming that the median time to achieve a negative COVID-19 RT-PCR was 15 days with placebo and 10 days with ivermectin.The full analysis set included all enrolled patients evaluated for efficacy at least once and excluded those who were ineligible after enrollment or did not receive the study drug. The safety analysis set included all enrolled patients, except those who did not receive the study drug.Categorical variables were expressed as the number and percentage of patients and continuous variables as mean (standard deviation [SD]) or median (range). The Kaplan–Meier method was used to summarize the number of events and time to a negative RT-PCR test. The number of patients with a negative RT-PCR test and point estimates and confidence intervals (CIs) of the median time to a negative RT-PCR test were calculated. CIs were estimated using the Brookmeyer and Crowley method . Data foSensitivity analysis for the primary outcome was performed by censoring 45-day data of patients who received rescue treatment and those excluded at the investigator’s discretion as being unable to continue treatment owing to worsening of symptoms. Exploratory analysis was conducted to assess the time to a negative RT-PCR test using survival analysis in subgroups stratified by age (<65 years/≥65 years) and presence/absence of COPD, diabetes mellitus, pneumonia, and smoking history. For secondary outcomes, the Cochran–Mantel–Haenszel test with the allocation adjustment factors as stratification factors was used to calculate adjusted odds ratios (ORs) and their 95% CIs to compare the differences between the ivermectin and placebo groups. Missing data were not imputed; however, no patient was excluded due to missing data.The number of patients with AEs was summarized using system organ class (SOC) and preferred term using the Japanese translation of the Medical Dictionary for Regulatory Activities. AEs were listed by severity .Statistical analysis was performed using SAS  version 9.4 (or later).3.3.1.n = 112, 43.6%) and placebo ; 107 patients in each treatment group received the study drug  withdrew consent and 248 were assessed for eligibility . Furtherudy drug . The fuludy drug . No deat3.2.Demographics and baseline characteristics were generally well balanced between the two treatment groups, with mean (SD) age (years) of 47.9 (15.1) and 47.5 (15.0) in the ivermectin and placebo groups, respectively. Most patients were male  and reported comorbidities at baseline . Over 70% of patients in both treatment groups had pneumonia at baseline . Nine patients (8.5%) and seven patients (6.6%) in the ivermectin group and the placebo group, respectively, had prior SARS-CoV-2 vaccination.The mean (SD) duration from the occurrence of a positive RT-PCR test to the administration of the study drug in the ivermectin and placebo groups was 2.7 (1.1) days and 2.7 (1.1) days, respectively; the corresponding mean (SD) duration from the first onset of a subjective symptom/first positive RT-PCR test to the administration of the study drug was 6.6 (2.6) days and 6.9 (2.8) days . The tre3.3.p = 0.785; HR <1 favors placebo, whereas HR >1 favors ivermectin). The median (95% CI) time to a negative RT-PCR test was 14.0 (13.0–16.0) days and 14.0 (12.0–16.0) days in the ivermectin and placebo groups, respectively. Of 106 patients in each treatment group, 82.1% (n = 87) and 84% (n = 89) of patients achieved a negative RT-PCR test result with ivermectin and placebo, respectively, over the follow-up period for up to 45 days  and placebo (21.7%) groups showed disease worsening  in the ivermectin group and hepatobiliary system disorders  in the placebo group. No patient reported a TEAE that led to study discontinuation.Overall, 29 patients (27.1%) in the ivermectin group and 28 (26.2%) in the placebo group reported AEs . Six patOne patient (0.9%) in the ivermectin group who reported a serious AE of the acute abdomen of grade 2 severity recovered. One patient reported blood creatine phosphokinase increased of grade ≥3 severity in the ivermectin group on day 15, and this TEAE was associated with the study drug; the patient did not improve, but the event was considered mild, and no follow-up was required based on the general condition of the patient. Two patients in the placebo group reported hepatic function abnormal of grade ≥3 severity, one of which was serious and of grade 4 severity; the condition of both patients improved .−1, respectively, in the ivermectin group (see The mean (SD) changes in systolic/diastolic blood pressures and pulse rate from baseline to day 15 were 0.3 (17.68) mm Hg/−2.5 (12.58) mm Hg and 81.0 (11.80) minroup see . The pro4.This is the first multicenter, double-blind, placebo-controlled RCT in Japan that investigated the efficacy and safety of a single intervention with ivermectin monotherapy in patients with COVID-19. The mean time from a positive RT-PCR test to study drug administration (2.7 days) and the treatment compliance rate (100%) were similar for both treatment groups. No significant difference in the time to a negative RT-PCR test was observed between ivermectin-and placebo-treated patients with mild-to-moderate COVID-19.p = 0.005)  of patients achieved a negative RT-PCR test result beyond the 15-day observation period, up to the end of the follow-up period. This result was observed with both ivermectin and placebo without any concomitant medication with potential efficacy for COVID-19. These results remained consistent in the sensitivity analysis and were similar to those from other RCTs with different dosages of ivermectin with/without concomitant treatments. In line with our study results with single-dose ivermectin 12 mg over a 15-day follow-up, the difference in the proportion of inpatients achieving a negative RT-PCR on days 5–6 with ivermectin versus placebo was nonsignificant with single doses of 12 mg/24 mg 2% of pat in otherThe disparity in the efficacy of ivermectin in patients with COVID-19 observed among different trials could be attributed to differences in patient characteristics, ivermectin dosage, exposures, and measured outcomes and to unidentified confounders. Studies that showed favorable outcomes with ivermectin for COVID-19 generally used a higher dose  or multiIn Japan, 6,832,377 confirmed cases of COVID-19 were reported until March 2022 , which iA recent report  that hig5.The current study showed that, compared with placebo, single-dose ivermectin treatment was well tolerated but did not lead to a decrease in the time to a negative RT-PCR test for patients with mild-to-moderate COVID-19.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by the IRB of Kitasato University Shirokane Campus, affiliation: Kitasato University. The patients/participants provided their written informed consent to participate in this study.KY contributed towards conceptualization and drafting the work. YK contributed towards conceptualization, drafting the work, and revising it critically for important intellectual content. MS, TM, JA, KN, MI, MT, MM, MK, and KK contributed towards conceptualization, drafting the work, and revising it critically for important intellectual content. MH, HA, MN, KI, ES, YI, NK, SK, SO, RK, KM, and YM contributed towards acquisition, analysis, or interpretation of data for the work, drafting the work, and revising it critically for important intellectual content. TW contributed towards conceptualization, acquisition, analysis, or interpretation of data for the work, and drafting the work. YO contributed towards the data analysis, drafting the work, and revising it critically for important intellectual content. All authors provide approval for publication of the content and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.This work was supported by public funding [AMED grant number: JP20fk0108159] from the “Research Program on Creation of Research Base for Emerging and Re-emerging Infectious Diseases: Development of Therapeutic Drugs for the Novel Coronavirus Disease (Covid-19)” of the Japan Agency for Medical Research and Development and “Kitasato Funding Project for Actions Against COVID-19” of the Kitasato Institute.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.A reviewer of this manuscript, Devan Mehrotra, is a biostatistician at Merck & Co., Inc., known as MSD outside the United States and Canada. The pharmaceutical ivermectin studied in this manuscript is manufactured and marketed by Merck & Co., Inc. in the United States under the brand name STROMECTOL®. After investigation, the journal has no reason to believe that the scientific conclusions of the article are affected in any way.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Cyclooxygenase (COX) and Lipoxygenase (LOX) are essential enzymes for arachidonic acid (AA) to eicosanoids conversion. These AA-derived eicosanoids are essential for initiating immunological responses, causing inflammation, and resolving inflammation. Dual COX/5-LOX inhibitors are believed to be promising novel anti-inflammatory agents. They inhibit the synthesis of prostaglandins (PGs) and leukotrienes (LTs), but have no effect on lipoxin formation. This mechanism of combined inhibition circumvents certain limitations for selective COX-2 inhibitors and spares the gastrointestinal mucosa. Natural products, i.e. spice chemicals and herbs, offer an excellent opportunity for drug discovery. They have proven anti-inflammatory properties. However, the potential of a molecule to be a lead/ drug candidate can be much more enhanced if it has the property of inhibition in a dual mechanism. Synergistic activity is always a better option than the molecule's normal biological activity. Herein, we have explored the dual COX/5-LOX inhibition property of the three major potent phytoconsituents  from Indian spices using in silico tools and biophysical techniques in a quest to identify their probable inhibitory role as anti-inflammatory agents. Results revealed the dual COX/5-LOX inhibitory potential of curcumin. Gingerol and capsaicin also revealed favorable results as dual COX/5-LOX inhibitors. Our results are substantiated by target similarity studies, molecular docking, molecular dynamics, energy calculations, DFT, and QSAR studies. In experimental inhibitory (in vitro) studies, curcumin exhibited the best dual inhibitory activities against COX-1/2 and 5-LOX enzymes. Capsaicin and gingerol also showed inhibitory potential against both COX and LOX enzymes. In view of the anti-inflammatory potential these spice chemicals, this research could pave the way for more scientific exploration in this area for drug discovery. Reducing risk factors might be considered an approach to tackling such chronic illness2. As evidence links many chronic diseases to inflammatory events, modulation of the inflammatory cascade appears to be a critical new approach to preventing these conditions3. Inflammation may be caused by various agents such as pathogens , external body injuries, chemicals or radiation effects, and a diseased condition . Severe inflammation, in some cases, might cause allergic reactions. Mostly all diseases are connected to inflammation, viz. arthritis (autoimmune disease), HIV (human immunodeficiency virus), psoriasis, TB (Tuberculosis), cancer, pneumonia, asthma, hypersensitivities, inflammatory bowel diseases, interstitial cystitis, vasculitis, chronic prostatitis, and many more4.Non-communicable diseases (NCDs) lie in the epicenter among all diseases as they represent the most prominent class in the death rate. The global chronic disease epidemic is responsible for the premature deaths of about 17 million people (80%) annually5. Eicosanoids, such as leukotrienes, prostaglandins, and thromboxane, are major inflammatory mediators, and their physiological production requires these enzymes.COX-1 is an essential enzyme that catalyzes the biosynthesis of eicosanoids which in turn promotes platelet aggregation, thromboxane, and vasoconstriction. It is mostly found in the kidney, stomach, and platelets6. Inhibition of this enzyme could help reduce biomarkers of inflammation. Non-steroidal anti-inflammatory drugs (NSAIDs), whether selective or non-selective, are commonly used as the first line of treatment in inflammatory diseases, because cyclooxygenase isoenzymes (COX-1 and COX-2) are responsible for the production of prostaglandins that play an important role in inflammation. They suppress the formation of pro-inflammatory eicosanoids. However, the long-term usage of these inhibitors may lead to bleeding, kidney failure, severe gastrointestinal disorders, and bronchospasm7. To avoid side effects, scientists had focused on developing selective COX-2 inhibitors in response to mounting evidence that this particular isoenzyme is over-expressed in inflammatory conditions, as opposed to COX-1 or combined COX-1/COX-2 inhibitors , which are associated with a wide range of unpleasant side effects8. Eicosanoids are released by the second essential biosynthetic pathway, activated by 5-lipoxygenase (5-LOX), an isozyme of LOX. Leukotriene B4, a mediator of atherosclerosis, cancer, and cardiovascular disease, is produced at the termination of the 5-LOX pathway. Thus, lowering leukotrienes by inhibiting 5-LOX may mitigate the risks of selective COX-2 and COX-1 inhibitors on the cardiovascular and gastrointestinal systems. With this perspective, it was anticipated that co-inhibition of cyclooxygenases (COX-I and COX-II) and 5-lipoxygenase (5-LOX) could lessen the cardiovascular and gastrointestinal side effects, while preserving the drugs' primary effectiveness against cyclooxygenase, particularly COX-210. Dual COX/5-LOX inhibitors offer numerous therapeutic benefits over traditional NSAIDs; firstly, they act on the two major arachidonic acid metabolic pathways and possess a wide range of anti-inflammatory activity. Secondly, dual inhibitors provide enhanced gastric protection and a safer cardiovascular profile, which is COX inhibitor’s most cumbersome side effect13. As a result, there has been significant effort in the field of dual-acting COX/5-LOX inhibitors in recent years, with very promising results. Darbufelone and Licofelone were developed and tested in humans as COX/5-LOX inhibitors. High toxicity and low efficacy prevented their commercialization, though. Hence, the need is to discover or repurpose drugs that could have dual COX/5-LOX inhibitory nature as both COX and LOX derivatives  are involved in other diseases than inflammation such as cancer proliferation wherein the use of dual inhibitors could be an interesting approach. In light of these ideas, it has been proposed that inhibiting the production of prostaglandins and leukotrienes could have synergistic effects and lead to the greatest reduction in inflammatory reactions possible12. Figure Many human diseases can be traced back to arachidonic acid cascade inflammatory mediators produced by the cyclooxygenase (COX) and lipoxygenase (LOX) pathways. Arachidonic acid (AA) is metabolized by a series of enzymes known collectively as cyclooxygenases COX-1/2) and lipoxygenases (LOX)/2 and li13. Numerous studies on plant whole crude extracts have been conducted over the past few decades, and it has been discovered that several plants have anti-inflammatory properties. Multiple inflammatory conditions can be effectively treated with compounds derived from plants. Alkaloids, flavonoids, polyphenols, and terpenoids are the few chemical classes linked to anti-inflammatory effects. Some well-known examples include andrograpanin, azadirachtin, Curcumin, embelin, resveratrol, and quercetin. Polyphenols are essential anti-inflammatory compounds found in spices that help improve health and decrease undesirable medication reactions. The discovery of flavonoids as a valuable substitute for traditional analgesics and anti-inflammatory agents with dual-inhibitory action, especially on COX-2 and 5-LOX, can reduce or resolve this issue and has attracted much attention. The anti-inflammatory compound flavocoxid, which contains the flavonoids catechin and baicalin, works by simultaneously inhibiting the COX 1/2 and 5-LOX enzymes14. Flavonoids are potent antioxidants that may regulate the oxidative generation of arachidonic acid from phospholipids, hence decreasing lipid peroxidation processes and activation of inflammatory pathways15. Flavocoxid suppresses inflammation not only by inhibiting cyclooxygenase (COX 1/2) and lipoxygenase (5-LOX), but also by lowering NF-B-induced production of pro-inflammatory cytokines16.In this regard, phytomedicine offer an excellent source of bioactive compounds that could serve the purpose above. Their bioactive constituents or natural products are resonably safer and more active than synthetic compounds17. Ginger and its phenolic compounds , sesquiterpenes have shown potent activity in reducing lung inflammation19. It is highly effective in inflammations associated with alimentary channel such as colitis and inflammatory bowel disease20. It also has clinical evidence in reducing the pain associated with osteoarthritis21. Polyphenols in green tea have anti-inflammatory effects. Green tea has been found in clinical research to lower inflammation related with cardiovascular disease, arthritis, and other inflammatory disorders22. It has also been demonstrated to be useful in lowering obesity-related inflammation and Alzheimer’s disease23. Capsaicin has anti-inflammatory activities24. In addition to these there are many other natural compounds which have clinically proven anti-inflammatory effects i.e. colchicine, resveratrol, epigallocatechin-3-gallate (EGCG), and quercetin25.Many natural products have shown proven efficacy towards preventing/curing inflammation associated with various diseases in clinical investigations. Turmeric and its curcumin-enriched preparations have been used to treat arthritis and alleviate its symptoms i.e. morning stiffness of joints30. Medical practitioners have used them to treat various ailments, including inflammation31. As per reports, culinary herbs and spices exert anti-inflammatory activities by activating PPARα and PPARγ, thereby causing inhibition the activation of NF-κB and enhancing the expression of anti-inflammatory cytokines32. Studies on animal inflammation models have demonstrated that curcumin and capsaicin delay the development and minimize the incidence and severity of arthritis by inhibiting TNF-α, IL-1β, IL-6, and NF-κB (inflammatory cytokines)33. Curcumin is superior to aspirin in this situation because it selectively inhibits the development of the anti-inflammatory TXA2 without side effects. Capsaicin down regulates the expression of the COX-2 enzyme and represses the inducible nitric oxide synthase (iNOS) to reduce PGE2 synthesis. It suppresses the synthesis of pro-inflammatory mediators, TNF-α and IL-6, following the elevation in the production of anti-inflammatory mediator IL-1034. It has been demonstrated that capsaicin modulates the NF-B signaling pathway bythe formation of pro-inflammatory cytokines. It exerts an anti-inflammatory effect by blocking the signaling path, coupling pro-inflammatory stimuli with cyclooxygenase activation, including inhibiting NF-κB nuclear translocation35. Gingerol, the bioactive constituent of ginger, is anti-inflammatory, as it inhibits PGE2 generation and reduces nitric oxide production. They ultimately activate the endogenous antioxidant defense system by enhancing the generation of anti-inflammatory cytokines and inhibiting the formation of pro-inflammatory cytokines36. According to the meta-analysis, ginger consumption reduces C-reactive protein (CRP), high sensitivity C-reactive protein (hs-CRP), and tumor necrosis factor(TNF-α) considerably37. A wide array of spices and their phytoconstituents remain unexplored for their pharmacological activity due to limited studies.This shifts our attention to the common culinary spices, as since time immemorial, they have played a very significant role due to their traditional importance in fields like Ayurveda and homeopathyCurcuma longa), ginger  and red pepper (Capsicum annuum) and identifying the possible mechanistic pathway (COX/5-LOX inhibition) through in silico and in vitro studies39. The need for new anti-inflammatory agents having dual mechanisms with improved safety profiles that prevent the release of both prostaglandins and leukotrienes encouraged us to repurpose a few anti-inflammatory agents  for their dual inhibitory activity to reclassify them as potent anti-inflammatory agents against both targets with a different probable mechanism of action. Their dual inhibitory activity and binding modes have been investigated using in silico tools41. Such a viewpoint opens the door, identifying culinary spices and herbs as a potential source of potent phytochemicals of the COX-1/2 and 5-LOX dual-route and offering it as an option in inflammation associated with various diseases. In view of the anti-inflammatory potential these spice chemicals, this study could pave a path for the scientific community to explore further in this direction, ultimately benefiting humanity.In order to explore the role of phytoconstituents of common culinary spices in preventing/curing inflammation associated with various diseases, herein we aim to examine the role of culinary spice phytochemicals like curcumin, gingerol, and capsaicin from turmeric 43.Target similarity represents the first step towards identifying dual inhibitors for any class. It helps identify structurally/functionally similar regions within proteins, which can justify it’s common evolutionary descent. Further, it supports the concept of dual inhibition by predicting the location and function of protein-coding and transcription-regulation regions in the genomic DNA by identifying conserved regions® Core™ i7-11,800 CPU@2.30 GHz processor, 16 GB RAM). All co-crystallized protein structures were obtained from the Protein Data Bank (PDB) database44. Various modules of Schr 45. The QSAR study was carried out usingHyperChem Professional 8.0.3 program.All simulation studies were performed on a DELL workstation running Ubuntu 20.04.3 LTS 47, and 5-LOX(PDB ID:3V99)48. The X-ray crystal structures of all proteins were downloaded from the Protein Data Bank49. The proteins were in complex with an inhibitor . The protein was prepared using the protein preparation wizard module of Schrödinger software50. The steps were addition of missing polar hydrogens, deletion of water molecules (beyond 5 Å) from hetero groups, ionization,generation of tautomeric statesat pH of 7.4 , optimization of hydrogen bonds [predicted by PROPKA (pKa prediction)], and finally energy minimization.Three pro-inflammatory receptors/proteinswereemployed in this study. These are COX-1 (PDB ID:3N8Y)Three phytocompounds, viz., curcumin, capsaicin, and gingerol were used as ligands for screening against COX-1, COX-2, and 5-LOX to determine their triple inhibitory potential. These ligand molecules are shown in Table All molecular docking simulation studies were performed using the Ligand docking program of the Maestro 12.3 module of Schrodinger. Before the initiation of docking, the active site coordinates on the target proteins were generated employing the Receptor Grid Generation module. The van der Waals radii scaling factor and partial charge cutoff were kept at 1.0 and 0.25. The remaining parameters were kept as default. The Extra Precision (XP) mode was used to perform molecular docking of the three phytocompounds in the active site of three respective proteins. The binding affinities and molecular interactions were analyzed, and data were recorded. Lastly, the protein–ligand complex interactions were visualized in 2D/3D, and images were saved for representation.https://www.swissadme.ch) webserver, wasemployed to compute the ADME values51. Predicting the in silico toxicity of a substance is essential for evaluating its safety profile. They not only aid in reducing the number of animal testing, but also help determine the maximum tolerable levels in animals. In this study, pkCSM web server (https://biosig.lab.uq.edu.au/pkcsm/prediction) was used to predict the pharmacokinetic properties/toxicities of our small molecules using graph-based signatures52. This server database provides various toxicity parameters, i.e. AMES toxicity, maximum tolerated dose, hepatotoxicity, skin sensitization, and hERG I and II inhibition. In both servers, the first step involves either uploading or writing the SMILES string of the particular molecule in the given space. The second step is to allow the server to run the program. Following this, the predictive data can be gathered and analyzed.The pharmacokinetic process known as absorption, distribution, metabolism, and excretion (ADME) describes how the body responds to a drug. Predictive in silico ADME data aid the drug development process since it aids lead optimization. Several software programs were used to forecast the in silico ADMET attributes for all the molecules. In this study, the Swiss ADME, developed and maintained by the Swiss Institute of Bioinformatics (SIB) (54. MDS was carried out using the Desmond module of the Schrödinger Suite developed by the D.E. Shaw research group (Academic license)55. The system builder panel was used to build the orthorhombic simulation box with Simple Point-Charge (SPC)56. The periodic boundary conditions were set at 10 Å from the outer part of the protein surface. The TIP3P water model was used to solvate the system. To the solvated system, sufficient counter ions were added to neutralize the system. The 0.15 M NaCl was added to the simulation panel maintained an isosmotic state. OPLS_AA force field was applied to the protein–ligand complex. Energy minimization was carried out till the system reached a stable condition . This equilibrated system was used for the final production of the MD simulation for 100 ns at 310.15 K temperatures at 1.0 bar pressure with NPT  ensemble while using default settings for relaxation before simulation. The results of the MD simulation were analyzed by generating a simulation interaction diagram. RMSD, RMSF, protein–ligand interaction diagram, interacting amino acid residues with the ligand in each trajectory frame, and the trajectory of different ligand properties were analyzed.Molecular dynamics simulation (MDS) helps study the protein–ligand complex’s structural stability and flexibility. In this study, MDS was performed for the top hit compounds to legitimize the protein–ligand complex (PLC) and measure the ligand-binding constancy in the active site of the selected targetthermal_MMGBSA.py script of the Prime/Desmond module of the Schrödinger suite was used. The post-simulation MM-GBSA analysis of free binding energy calculation was carried out with the generation of 0–1000 frames. Two hundred frames were processed and analyzed throughout the MM-GBSA calculation of 100 ns MDS data. The binding free energies  were then calculated.Post-simulation MM-GBSA analysis was carried out. The 58. Following the optimization, analysis was done using the Gauss View 6.0.16 program .As one of the most reliable theoretical methods, density functional computations were used to conduct DFT experiments using the Gaussian09 suit programs45. In this study, calculations were performed using B3LYP and exchange–correlation functional with a basic set of 6-31G  for the atoms of carbon, nitrogen, oxygen, and hydrogen, and a theoretical geometry was established. For the ligands under study, DFT/B3LYP 6–31 + G  basis sets were used to assess single-point energy and dipole moment (D) values61. Calculated variables included the ionization energy, electron affinity, electronegativity, electronic chemical energy, chemical hardness, chemical softness, and electrophilicity index. Using DFT analysis, the energy difference between the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energies were used to study the interactions of the ligand/s with the proteins62. The study considered the active site of the protein and examined the binding energies of the interacting residues.DFT calculations help to investigate the electronic/nuclear structure of various systems. Based on quantum mechanical concepts, DFT computations enable predicting and estimating the system’s behaviorThe QSAR study was carried out using HyperChem Professional 8.0.3 program. The ligands were optimized by applying force field (MMþ) using semi-empirical PM3 methods. Energy minimization was carried out using the Fletcher–Reeves conjugate gradient algorithm. In addition, various other parameters like volume, hydration energy, Log P, refractivity, polarizability, mass, total energy, dipole moment, and RMS gradient were also computed for all the ligands under study.65. The inhibitory activity was measured and reported as IC50 value (µM). Data are expressed as IC50 ± SEM for triplicate studies.The inhibitory activities of the test compounds against COX-1 and COX-2 enzymes were determined by enzyme immune assay (EIA) kits  using COX-1 (ovine) and COX-2 (human recombinant) according to reported methods50 values were measured in µM according to the manufacturer’s instructions and reported method65. Data are expressed as IC50 ± SEM for triplicate studies.The ability of the test compounds to inhibit the 5-LOX enzyme was evaluated using the Human Lipoxygenase Inhibitor screening assay (EIA) kit . The ICFor dual inhibition of COX-1/2 and 5-LOX, all the major proteins involved in the inflammation pathways were selected based on literature review as we searched for significant proteins that influence inflammation. Keeping in pace with the concept of dual inhibition, three targets (COX-1/2 and 5-LOX) were selected,as presented in Table Sequence alignment was done for all three targets under study. Figure 66. The presence of multiple electropositive micro-domains being close to each other could favor the binding of small molecule inhibitors. The active site pocket of the COX-2 binding site is 20% larger than that of COX-1, whereas, in the case of 5-LOX,the active site is fully encapsulated. In the latter case, the amino acid residue Phe177plays a critical role and helps in the substrate/inhibitor binding in the active site67.To validate the results of target similarity, it is very important to locate the binding sites on the proteins to access the ligand interaction. For this, we evaluated the electrostatic surface potential (ESP) of the three studied proteins, as shown in Fig. 3Further, His600 helps in substrate catalysis. Hence, the structural motif of 5-LOX is termed a “twist-and-pour cap”. Thus, from the above images and discussion, it can be interpreted that these three proteins share a high degree of similarity. Hence for our study, these three targets represent the primary class of proteins involved in the inflammatory pathways. This also justifies the rationale behind the design of dual inhibitors targeting COX-1/2 and 5-LOX.68.The validation/re-docking studies Fig.  on the cThe ligand docking study with COX-1/2 and 5-LOX enzymes revealed favorable binding energies. The binding affinities/docking scores of all molecules under investigation have been presented in Table Structurally, there were relative correlations among the number of H-bonds. Upon binding with 1CVU, curcumin had two H-bonds with Ser530 and Tyr355, gingerol had two H-bonds with Met522 and Tyr335, and capsaicin had two H-bonds with Val349 and Met522 Figs. ; Table 469. Moreover, the position of the co-crystallized ligand and the three test compounds inside the catalytic site of COX-2 was stabilized by surrounded hydrophobic interactions.Due to the high homology of binding site residues, various interactions were conserved. The high docking score of curcumin could be attributed to the formation of H-bonds and different other interactions, i.e., hydrophobic and polar. Apart from conserving the H-bond with Ser530, curcumin was able to make a new H-bond with Tyr355. This new H-bond formation might allow deeper penetration into COX-2 protein. It has also been observed that the COX-2 binding site was 20% larger than that of COX-1. Apart from establishing H-bonds, it also showed varied hydrophobic interactions with a variety of amino acid residues . These interactions play a major role in stabilizing the ligands at the binding interface. Moreover, the strong binding affinity could be attributed to the presence of polar residues, namely Arg120, Ser353, Tyr385, Gly526, and Glu524 that enhance the chances of squeezing the molecule into the active site. To sum up, curcumin’s selectivity to COX-2 was primarily due to the binding to the hydrophobic region and extending to the lobby region near the entrance of the COX-2 binding site, thereby forming hydrogen bonds with Ser53070.To understand the binding modes of ligands in COX-1, it is crucial to have a detailed analysis of the various interactions at the residues level. Figures 69. Capsaicin and gingerol displayed less binding energies and interactions with the COX-1 binding site. This could be attributed to the incompatibility between the narrow groove (Arg120 to Tyr355) and the bulkiness of the molecules. There might be an inconsistency in their positions and interaction patterns concerning curcumin and internal ligand. Based on the analysis of interactions, curcumin seems to inhibit COX-1 prominently.The curcumin ligand’s backbone makes various hydrophobic contacts, viz. Val349, Leu352, Trp387, Leu384, Phe381, Leu531, Met522, Ala527, Ile523, Val116, Leu93, Pro86, and Ile89, and few polar interactions viz. Ser353, Tyr355, Tyr385, Ser530, Gly526, and Glu524. This encloses the curcumin molecule in a cage-like structure, justifying its COX-1 inhibition and increasing its selectivity71. Figures +2) which forms the center of the active site. The active site cavity appears to have a partial U-shaped cavity lined with highly conserved Leu and Ile residues. However, 5-LOX protein has a unique catalytic cavity with no substrate access site due to the shielding effects of outer solvents. It comprises the side chains of residues Phe177 and Tyr181 positioned to impede access into the catalytic cavity. The presence of catalytic iron in 5-LOX acts as both an electron donor andacceptor72.The 5-lipoxygenase (5-LOX) enzyme is responsible for the initiation of biosynthesis of pro-inflammatory leukotriene lipid mediators and the synthesis of anti-inflammatory lipoxins+2 cofactor of the 5-LOX enzyme was bound to capsaicin and gingerol, which causes the chelation of these molecules. Besides, all ligands were mostly enclosed around hydrophobic residues. These hydrophobic interactions  were primarily conserved in all cases, along with polar interactions (histidine and serine residues). Though all ligands bind effectively with 5-LOX, the number of H-bonds made by curcumin is more than other ligands under study. It was stabilized by surrounded hydrophobic and polar interactions, contributing to a slightly higher 5-LOX inhibition than different ligands. Hence, curcumin effectively binds to the active site and inhibits 5-LOX activity that might reduce leukotriene production thereby reducing the inflammatory responses.Compared to the internal ligand, wherein hydrogen bonds were formed, curcumin, capsaicin, and gingerol were interactive enough to establish H-bonds. Curcumin had three H-bonds with Lys409, Ala672, and Phe177, capsaicin had two H-bonds with Val671 and Phe177; and gingerol had two H-bonds with Asn554 and Phe177. These interactions help the ligands access the active site via the α2 helix. The H-bond with Phe177 is critical in providing a fully functional active site.FeTo conclude, among all three ligands under study, curcumin could be considered the best-fit molecule for all three proteins. The geometry of curcumin allows its insertion into the active site of all three proteins, with hydrogen bonding potential, i.e. acceptor/donor groups that improve the affinity for COX-1/2 and 5-LOX. From the above discussion, it is evident that curcumin has the potential to block both COX-1/2 and the 5-LOX enzymes. It generally acts by blocking the formation of both prostaglandins and leukotrienes, but does not affect lipoxin formation. Such combined inhibition might avoid some disadvantages of selective COX-2 inhibitors and spares the gastrointestinal mucosa.http://www.swissadme.ch/). The obtained ADME properties are presented in detail in Table 75. Results indicated that the molar refractivity, which accounts for the overall polarity of the molecules, was 102.80, 90.52, and 84.55 for curcumin, capsaicin, and gingerol, respectively; these values were in the normal range (30–140)76.The topological polar surface area (TPSA) was 93.06, 58.56 and 66.76 Å2 for curcumin, capsaicin, and gingerol, respectively. This refers to the molecule’s ability to permeate cells, i.e. molecules with TPSA > 140 Å2 are poor at cell permeation. A TPSA < 90 Å2 is usually preferred for molecules to permeate the BBB and act on CNS76. This suggests that curcumin cannot cross the blood–brain barrier (BBB), while the other two can.Regarding drug properties that affect ADMET, “solubility class lipophilicity” refers to a molecule's ability to dissolve into a lipophilic medium77. These properties include permeability, ADME, solubility, plasma protein binding, and toxicity. Results of iLOGP78 and SILICOS-IT suggested that the iLOGP values of all the molecules were in the acceptable range (− 0.5 to + 5.4), while SILICOS-IT values for all the identified leads were in the most favorable range (− 10 to 0). Water solubility is a significant factor determining a drug’s distribution and absorption. Log S calculations show the molecule’s solubility in water at 25 °C. The ESOL model’s calculated log S values shouldn’t be more significant than six for appropriate solubility79. Curcumin, capsaicin, and gingerol showed log S values of − 3.94, − 3.53, and − 2.96, accounting for a good solubility profile. According to the findings above, all of the lead compounds showed a good balance between permeability and solubility and may exhibit acceptable bioavailability when taken orally. All the molecules revealed a high predicted GI absorption80. Understanding the results of ADMET and cell-based bioassays is aided by permeability predictions81. Results indicated that the permeability over human skin was -6.28, -5.62, and -6.14 cm/s for curcumin, capsaicin, and gingerol, respectively. These predicted values were in the acceptable range81.Drug interactions caused by metabolism can occasionally reduce a drug’s bioavailability. Drug-metabolizing enzymes can only bind to the drug in its free form. As the most significant class of metabolizing enzymes, cytochrome P450 enzymes (CYPs) must be studied to understand the metabolic behavior of studied molecules. The CYPs (CYPs of human liver microsomes (HLM)) inhibitory activity of all the lead compounds was evaluated82. Only curcumin showed a good profile in this regard. A molecule's drug-likeness indicates whether it has the potential to develop into an oral medication.All three ligands under study were evaluated for their ADMET profile using online web server i.e. Swiss ADME  to 10 (very tough)).Thus, from the above data, it is evident that all molecules predicted ADME data are within the recommended values, with curcumin being the best.In our investigation, the drug-likeness was determined using five distinct filters. All molecules displayed no violation of drug-likeness rules and had a bioavailability score of 55% (good bioavailability). The Abbot Bioavailability score, which can be determined by a feasibility score of 11%, 17%, 56%, or 85%, forecasts the fate of a chemical for an experiment including quantifiable Caco-2 cell line permeability or 10% oral bioavailability (in rats)https://biosig.lab.uq.edu.au/pkcsm/prediction). The AMES test, which uses microorganisms to determine a chemical compound's projected mutagenic potential, produced positive results for all molecules, revealing no AMES toxicity. The most significant dose that most patients can accept is technically known as the maximally tolerated dose (MTD). Herein the maximum tolerated dose (human) was 0.081, 0.46, and 0.0635 Log mg/kg/day for curcumin, capsaicin, and gingerol, representing a moderate dosage level per protocols, with curcumin being the most potent one. hERGI and II (human Ether-a-go-go-Related gene) codes for proteins that regulate ion channels responsible for the cardiac electrical action potential of the heart. Hence, drugs under study should prohibit their inhibition during drug development. Only curcumin showed no hERGI and II inhibition, negating ventricular arrhythmias' probability. For curcumin, capsaicin, and gingerol, the Oral Rat Chronic Toxicity (LD50) values were 1.833, 2.065, and 1.958 mol/kg, respectively, while the Oral Rat Chronic Toxicity (LOAEL) values were 2.228, 1.827, and 1.631, respectively, indicating a good safety profile. All molecules (curcumin and gingerol) except for capsaicin were predicted to be non-hepatotoxic. All molecules showed no skin sensitization as well. T. Pyriformis toxicity and Minnow toxicity were at acceptable levels. Results of the predicted toxicity of all molecules under study are shown in Table All three molecules  under study were evaluated for their toxicity profile using an online web server, pkCSM . MD studies are usually implemented to study the nature of macromolecules or analyze the physical movements of atoms and molecules. The obtained simulation findings are discussed below.The RMSD values were substantial for all protein–ligand conformations. RMSD analysis indicates whether the simulation has equilibrated around a fixed value. In complex A, the protein RMSD was stable throughout the simulation with minor fluctuations between . The variation was in the range of 1.6–2.8 Å.How stable a ligand is in relation to a protein and its binding pocket is shown by the ligand RMSD (right Y-axis). Ligand RMSD was highly unstable as it showed significant fluctuations with 2.0–3.5 Å from 0 to 40 ns which then stabilized, showing 5–8 Å till 100 ns with some minor deviations Fig. A. For coFor describing local variations along the protein chain, the RMSF is helpful. Peaks on this diagram represent regions of the proteins that vary most during the simulation. Usually, the protein’s N- and C-terminal tails move more than any other area. Because they are often more rigid than the protein's unstructured portion, parts of secondary structures like alpha helices and beta strands change less than loop sections. The RMSF of 3N8Y protein Fig. A looks sThe trajectories of different properties of co-crystal ligand-enzyme complexes  during the 100 ns of MD simulation are depicted in Fig. Protein–ligand contacts were monitored throughout the simulation. H-bonds are essential for ligand binding. Because they have such a significant impact on drug specificity, metabolization, and adsorption. Hydrogen-bonding properties must be taken into account while developing new drugs. Herein complex A Fig. A showed The interactions and contacts  presented andare shown in the form of a timeline in Fig. The average change in displacement of a particular set of atoms for a given frame relative to a reference frame is calculated using the RMSD values. For each frame in the trajectory, it is computed. The RMSD values are aligned with the protein-internal ligand confirmations. In complex A Fig. A, the prRMSF analysis helps in understanding the local variations along the protein chain. Usually, peaks represent the residues that fluctuate the most during the simulation. The primary structures of a protein typically move more than the secondary structures as they are more rigid.The fluctuations in the protein-curcumin complex were almost the same as in the protein-internal ligand case. Hence this shows the complexes’ overall stability. Herein the RMSF of 3N8Y protein Fig. A looks sThe trajectories of different properties of curcumin-enzyme complexes  during the 150 ns of MD simulation are depicted in Fig. Protein–ligand contacts refer to the interactions between the protein and ligand during the simulation period. They can be classified under four major headings: H-bonds, hydrophobic interactions, ionic interactions, and water bridges. The bars represent the % of the simulation time the specific interactions were maintained. Herein complex A Fig. A showed The interactions and contacts  presented and shown in the form of a timeline in Fig. The three ligands were subjected to a MM-GBSA analysis to determine the binding energies between the ligands and the proteins. It helps determine the protein–ligand complex’s stability after it is bound to the active site. Herein, the dG value represents the difference in energy between the optimized protein–ligand complex (prime) energy and a combination of optimized free ligand and protein energy. The detailed MM-GBSA dG binding values have been presented in Table The MM-GBSA analysis revealed that curcumin had the best binding energy with all the three proteins under study . These binding energies were relatively better than the co-crystallized protein–ligand complex, highlighting curcumin’s potential in binding and inhibiting all three proteins under study.The DFT analysis investigated the binding affinity pattern of the ligands under investigation as it revealed electronic structural characteristics of the ligands. Using Koopman’s theorem, all three ligands were optimized to the B3LYP/6-311G level, and their molecular orbitals were investigated. Table Furthermore, positive chemical hardness and chemical softness values enhance compound’s molecular interactions. Other metrics, such as polarizability (r) and electrophilicity (x), quantify the tendency to take an electron from its surroundings as a positive amount. Figure The QSAR analysis showed several critical aspects related to the chemical structures of the phytocompounds. Table 50) was determined using serial dilution method  for each compound. Ibuprofen, celecoxib, and zileuton were used as reference drugs for COX-1, COX-2, and 5-LOX, respectively. From results presented in Table Colorimetric enzyme immunoassay (EIA) was used to assess the inhibitory potential of the test compounds against COX-1, COX-2, and 5-LOX enzymes. The test concentration which produces 50% inhibition  are comparable to some extent with ibuprofen and celecoxib (COX-I and COX-2 inhibitors) and zileuton (5-LOX inhibitor). However, curcumin possesses the best dual inhibitory activity towards COX-1/2 (IC50 = 18.58 ± 0.44 µM/13.42 ± 0.92 µM) and 5-LOX (IC50 = 24.08 ± 0.63 µM) enzymes. The in vitro inhibitory activities thus confirm the predicted inhibition of COX-1/2 and 5-LOX enzymes in molecular docking studies. Experimental investigation thus validates the in silico inhibition study.Among the three compounds, curcumin exhibited the best inhibitory activity against COX-1/2 and LOX enzymes. The inhibitory activities of capsaicin and gingerol were also prominent, but the extent of inhibition was less as compared to curcumin. When compared with the standard drugs, the ICThe currently available therapeutics, i.e. NSAIDs that target various enzymes in the inflammation pathways, suffer from side effects. There is an urgent need to rediscover a single molecule with intense synergistic anti-inflammatory activity. This could only be possible through “dual inhibition of enzymes” in the inflammation pathway. In this perspective, we focused on exploring the role of culinary spice chemicals as dual inhibitors of key enzymes, COX and LOX in inflammatory pathways. There are strong evidences that several culinary spices could exert health benefits with strong antioxidant and anti-inflammatory properties.40. For example, several studies have shown that the combination of curcumin (major bioactive from turmeric) and piperine (major bioactive from black pepper) results in a synergistic inhibition of cancer cell growth. This is thought to occur because piperine enhances the bioavailability of curcumin, allowing it to more effectively target cancer cells. Another example is the combination of berberine  and metformin (a synthetic drug used to treat diabetes). It has been reported that the combination of these two compounds results in a greater reduction in blood sugar levels than either compound alone, suggesting a synergistic inhibition of diabetes. On this note, the concept of synergistic inhibition of natural products holds promise for the development of more effective and efficient therapies for a range of diseases and conditions87.This paves the way to employ the phytoconstituents of culinary spices as potent inhibitors of the enzymes involved in the inflammation pathways. Developing new and selective COX inhibitors requires knowledge of COX structure and isoform specificity. COX’s active site comprises a lengthy hydrophobic channel with a limited entry at the membrane-binding domain. Despite the fact that their active sites are quite similar, their binding cavities differ, with COX-2 having a bigger binding cavity than COX-188. This created opportunities for the creation of specific COX inhibitors. The selectivity of COX inhibitors is determined by interactions with three distinct areas of the enzyme’s active site: the Arg120/Tyr355-containing entrance region, the hydrophobic pocket directly below the haem group, and the side pocket. Highly conserved sections include the active site entrance and the hydrophobic pocket; however, the side pocket is non-conserved, with a few different amino acid residues that give rise to an additional pocket in COX-2.COX-1 and COX-2 have highly similar active sites89 However, COX-2 has a side pocket just above the COX binding site’s entrance (Arg120 and Tyr355). The amino acids that comprise the 5-LOX binding site are primarily hydrophobic and form a deep-bent shape. Reports have indicated that Phe177 and Tyr181 in the cleft's top region form part of the 5-LOX binding site, with Trp599 and Leu420 at the cleft's base90. In addition, the polar amino acid Lys409 is present at the entrance of 5-LOX. The research suggested numerous other amino acid residues, including Tyr181, Leu414, Asn425, Arg411, and Phe421, are crucial for the interaction between 5-LOX and its substrate67. Hence, dual inhibition could be achieved by simultaneous inhibition of these two enzymes11.Synergistic inhibition occurs when two or more bioactive compounds work together to enhance each other’s inhibitory effects, resulting in a greater overall effect than would be achieved by either compound alone. This can occur through a variety of mechanisms, including increased potency, increased bioavailability, and enhanced target specificityπ-π interactions between the ring B of the flavanone and Tyr355 at the entrance to the COX binding site are responsible for the COX-2 selectivity of curcumin. The hydrophobic contacts between the COX enzyme’s hydrophobic region and the lobby region further promoted effective binding. Curcumin’s expected activity in blocking 5-LOX comes from the hydrogen bond interactions withLys409, Ala672, and Phe177. This H-bonding was absent in the case of co-crystallized ligand. The hydrophobic and polar interactions were the primary contributors to the effective binding of curcumin. Molecular dynamics data further strengthened the results of molecular docking. Results revealed the high stability of curcumin-protein complexes despite minor acceptable deviations. Upon binding of curcumin in the protein’s active site, the complex displayed minor RMSD values. MMGBSA binding energies were quite better than the co-crystallized protein–ligand complex, highlighting curcumin’s potential in binding and inhibiting all the three proteins under study68. The other two spice chemicals, capsaicin and gingerol, displayed good binding modalities with the proteins under study. This study also explored the pharmacokinetics (ADMET) and the chemical interactions of the ligands under study, with various proteins and curcumin being the good among all. The present investigational data is also supported by DFT and QSAR studies.As per our study, curcumin, the active ingredient of turmeric, displayed significant dual COX-1/2 and 5-LOX inhibitory activity. The results of molecular docking and dynamics studies can justify this. Hydrogen bond interactions with critical amino acids, such as Ser530 in the COX active site and There are some limitations regarding the analysis of natural products for inflammation associated with various diseases: (1) Complexity of natural products: Natural products are often complex mixtures of various compounds, which makes it difficult to isolate and identify the active compounds responsible for their anti-inflammatory effects. This complexity can also make it challenging to determine their mechanisms of action. (2) Lack of standardization: There is often a lack of standardization in the preparation and use of natural products, which can lead to variability in their potency and efficacy. This makes it difficult to compare results across different studies and to establish consistent dosing regimens. (3) Limited understanding of mechanisms of action: While natural products have been used for centuries to treat inflammatory conditions, the exact mechanisms by which they exert their anti-inflammatory effects are often poorly understood. This limits our ability to predict their efficacy in different disease contexts and to develop new drugs based on their mechanisms of action. (4) Limited clinical evidence: While there is a growing body of preclinical evidence supporting the anti-inflammatory effects of natural products, there is often a lack of well-designed clinical trials to support their use in humans. This makes it difficult to establish their safety and efficacy in clinical settings and to obtain regulatory approval for their use. (5) Interactions with other drugs: Natural products can interact with other drugs and medications, which can lead to unintended side effects or reduced efficacy. These interactions can be difficult to predict and may vary depending on the specific natural product and the individual patient.Overall, while natural products show promise as potential treatments for inflammatory conditions, there are still many limitations to their use and further research is needed to fully understand their mechanisms of action and potential clinical applications.Our study pioneered investigating the role of culinary herbs as a potential inhibitor of the enzymes (COX and LOX) associated with inflammation. Herein our study identified a spice chemical, curcumin as a potential dual inhibitor of COX-1/2 and 5-LOX. This perspective identifies curcumin as a possible phytoconstituent of the COX-1/2 and 5-LOX dual pathway and offers it as potential drug inflammation-associated diseases. Overall, the curcumin has the potential for the dual inhibition of COX-2 and 5-LOX. In experimental inhibitory (in vitro) studies, curcumin exhibited the best dual inhibitory activities against COX-1/2 and 5-LOX enzymes. Capsaicin and gingerol also showed inhibitory potential against both COX and LOX enzymes. This study also highlights the important role of culinary herbs and Indian spices in inflammation associated with various disorders. In addition, our study also provides compelling support for the idea that consuming a diet rich in certain anti-inflammatory spices  can help reduce inflammation and protect against diseases associated with chronic inflammation. Although these spices have been used for millennia to alleviate inflammation, more research is needed to determine whether or if they may be used therapeutically to prevent or treat inflammatory conditions. In view of the anti-inflammatory potential these spice chemicals, this research could pave the way for more scientific exploration in this area for drug discovery.


To study the impact of age and the interval between disease diagnosis and death on the organotropism of SARS-CoV-2.Patients underwent post-mortem biopsies from lungs, Waldeyer ring, heart, liver, kidneys and bone marrow between 2020‒2021. SARS-CoV-2 organotropism was mapped by using molecular RT-PCR analyses for SARS-CoV2 targeting the Envelope gene (E), the RNA Polymerase Gene (RdRp), and the Nucleocapsid gene (N). Statistical and linear regression analysis was performed to study the impact of age and illness duration in SARS-CoV-2 organotropism.p < 0.001).We performed 158 postmortem biopsies in 21 patients, with a mean age of 76 years old. The mean interval between the diagnosis of the infection to the death was 23 days. The RNA of the SARS-CoV-2 was detected in 100% of lung biopsies, 76%‒82% of Waldeyer’s ring biopsies, 55% of heart biopsies, 40% of kidney biopsies, 33% of liver and 25% of bone marrow biopsies. Patients who died before the day 9, presented extensive visceral dissemination of SARS-CoV-2 RNA. Most of the patients older than 80 years (90%) presented visceral dissemination of SARS-CoV-2 RNA, while among younger patients, only 3/11 patients presented visceral dissemination of the virus. The relationship between “age” and “illness duration” and multitropism of the virus was statistically significant (Disease interval and age were factors that were significantly associated with RT-PCR positive results in multiple organs. Critical COVID-19 patients have multiorganic viral dissemination in early stages. In the critical older patients, multiorganic viral dissemination is the rule.4. Case Series. SARS-Cov-2 disease is an airborne disease that mainly affects the respiratory tract. Its high contagiousness is the reason that since the first case in Wuham in December 2019, it generated a worldwide pandemic in just a few months. The infection generates an uncontrolled autoimmune inflammatory response (cytokine storm) in the patient, responsible for visceral damage.Currently is known that Angiotensin-Converting Enzyme (ACE-2) which acts as a viral host cell entry receptor, is present in several organs, which is why SARS-CoV-2 can cause systemic disease with involvement of the kidneys, heart and blood vessels, liver, pancreas and the immune system.The aim of this study was to determine the impact of age and the interval between disease infection and death on the organotropism of SARS-CoV-2.This study was approved by the institutional Review Board. All cases were recruited from a single center and met the clinical diagnostic criteria for COVID-19 infection with a positive PCR test from nasopharyngeal swabs at the time of admission. With permission from the patients’ families, post-mortem biopsies were taken in the COVID-19 Intensive Care Unit (ICU) or COVID-19 Unit while wearing personal protective equipment.Biopsies from the Waldeyer’s Ring (WR) tissue, palatine tonsil, adenoids, and posterior oropharynx mucosa were taken by an otorhinolaryngologist. The visceral organs, including the lungs, liver, kidney, bone marrow, and heart were biopsied by a radiologist using ultrasound guidance. All specimens included in this current study were obtained within 1 h of death in order to avoid post-mortem degenerative changes.The samples were sent in physiological serum to the molecular laboratory for RT-PCR analyses for SARS-CoV2. An Allplex™ 2019-nCoV PCR assay  targeting the Sarbecovirus Envelope gene (E), an Ribonucleic Acid (RNA) Polymerase gene (RdRp), and SARS-CoV-2 RNA-dependent Nucleocapsid gene (N) was employed. The programe JASP (version 1.16.0.0) was used to perform the statistical and linear regression analyses.We performed 158 postmortem biopsies in 21 patients (14 men), with a mean age of 76 years-old (range 53‒99 years). The mean interval between the illness onset to the death was 23 days (range 2‒65 days).The RNA of the SARS-CoV-2 was detected in 100% of lung biopsies, 76%‒82% of WR biopsies, 55% of heart biopsies, 40% of kidney biopsies, 33% of liver and 25% of bone marrow biopsies .Figure 1When analyzing the age of the patient with the results of the biopsies performed, we observed that in older patients, it is when there is greater visceral involvement . Viscerap < 0.05). The relationship between the number of positive biopsies per patient and the factors age and disease interval were statistically significant, with a greater number of positive tissues being found in older patients (p = 0.015) and those with a shorter disease interval (p = 0.002)  .Figure 4p = 0.041), and the shorter interval until death, (t(20) = −2.42, p = 0.026), the greater the dissemination in the upper airways  = 2.20,  airways .Figure 5p = 0.014).In visceral organs with hematic dissemination, only the days variable is statistically significant: the fewer days elapse between the diagnosis of the infection and the biopsy, the greater the number of positive visceral organs with hematogenous dissemination (t(20) = −2.73, The early stage of SARS-CoV-2 infection is characterized by virus replication and a viremia phase, as a potential manifestation of a systemic infection.The role of the WR system in the pathogenesis of other viral infections is well known given that microorganisms penetrate these cells and start replicating there.It is known that the SARS-CoV-2 virus mainly affects respiratory and immune systems but that other systems are not spared, especially in elderly patients.We consider that both age of the patient and disease interval are determining factors in the viral detection at the different organs studied being the disease interval more significative than the age regarding systemic infection. The viability of this virus after a prolonged interval remains unknown.Disease interval and age were factors that were significantly associated with RT-PCR positive results in multiple organs. Using RT-PCR, the presence SARS-CoV-2 RNA was widely spread throughout the bodies of patients who died after a short COVID-19 disease interval. In the group of patients who died later than 25 days after diagnosis of infection, the RT-PCR detected SARS-CoV-2 only in the lung and WR tissue, even after intervals of up to 65 days.This is a multidisciplinary study, many specialists have participated in the design, methodology, taking biopsies from patients, processing, data analysis, and conclusions.We have selected 8 of all the contributors to the paper, I kindly ask you to accept the authorship.None.Data sharing is not applicable to this article as no new data were created or analyzed in this study.The authors declare no conflicts of interest.


Cardiovascular disease (CVD) is a leading cause of morbidity and mortality globally. The extent to which CVD affects the population’s health varies across countries. Moreover, quantitative estimates of the trend of inequalities in CVD burden remain unclear. The objective of our study was to assess the socioeconomic inequalities and temporal trends of CVD burden across 186 countries and territories from 2000 to 2019.We extracted data from the Global Burden of Disease, Injuries, and Risk Factors Study (GBD) 2019, and conducted a cross-national time-series analysis. Age-standardized disability-adjusted life-year (DALY) rates were used to measure the burden of CVDs, and gross national income (GNI) per capita was used to approximate the socioeconomic development. Concentration curves and concentration indexes (CIs) were generated to evaluate the cross-national socioeconomic inequality of CVD burden. A joinpoint regression analysis was used to quantify the changes in trends in socioeconomic inequality of CVD burden from 2000 to 2019.The age-standardized DALY rates of CVDs decreased in 170 (91%) of 186 countries from 2000 to 2019. The concentration curves of the age-standardized DALY rates of CVDs were above the equality line from 2000 to 2019, indicating a disproportional distribution of CVD burden in low-income countries. The CIs declined from − 0.091 (95% CI: −0.128 to − 0.054) in 2000 to − 0.151 (95% CI: −0.190 to − 0.112) in 2019, indicating worsened pro-poor inequality distributions of CVD burden worldwide. A four-phase trend of changes in the CIs of age-standardized DALY rates for CVD was observed from 2000 to 2019, with an average annual percentage change (AAPC) of − 2.7% (95% CI: −3.0 to − 2.4). Decreasing trends in CIs were observed in all CVD subcategories but endocarditis, with AAPC ranging from − 6.6% (95% CI: −7.3 to − 5.9) for ischemic heart disease to − 0.2% (95% CI: −0.4 to − 0.1) for hypertensive heart disease.Globally, the burden of CVD has decreased in more than 90% of countries over the past two decades, accompanied by an increasing trend of cross-country inequalities. Moreover, the overall burden of CVD continues to fall primarily on low-income countries.The online version contains supplementary material available at 10.1186/s12939-023-01988-2. Cardiovascular disease (CVD) is the most prevalent chronic disease, with 11 major subcategories such as rheumatic heart disease, ischemic heart disease, stroke, and hypertensive heart disease. It is a leading cause of death and disability worldwide . AccordiThe burden of CVD is the result of a synergistic combination of unhealthy lifestyles and poor access to and performance of healthcare systems. In response to the rapidly increasing burden of CVD due to the epidemiological transition, the WHO launched the 25 × 25 Global Action Plan in 2013 . CountriThe Global Burden of Disease, Injuries, and Risk Factors Study (GBD) evaluated the burden of CVDs in 204 countries and territories worldwide , providiIn line with the Sustainable Development Goals (SDGs) to improve global health, measuring socioeconomic inequalities in CVD burden and the temporal trends in response to the ever-increasing population with CVD is crucial to ensure the equity and effectiveness of CVD prevention . This haThis study is characterized as a cross-national, observational, and cross-sectional investigation. The study collected data on the socioeconomic development and CVD burden from 186 countries/territories spanning the years 2000 to 2019 with an aim to assess inequalities and temporal trends in CVDs and its 11 subgroups.GBD 2019 provides comprehensive, multi-national, and multi-institutional global collaborative epidemiological research for estimating the burden of 369 diseases and injuries by sex and age group for 204 countries and territories from 1990 to 2019 . We usedGross National Income (GNI) per capita was used as a proxy for national socioeconomic development. GNI has been recognized as a good indicator to measure the country’s economic strengths, needs, and the general living standard of its average citizens . We collWe used the concentration curve and concentration index (CI)  to quantp-value; the statistical significance level is corrected by the Bonferroni method for greater consistency in the p-value. Values are considered statistically significant if APC and AAPC are different from zero at an alpha of 0.05.For this study, we did an international, time-series secondary analysis of GBD 2019. To characterize the cross-country burden of CVD and its 11 subcategories, a descriptive analysis was done. We calculated the change rate of age-standardized DALY rates from 2000 to 2019, and divided countries into four groups: disease burden increased by 15% or over, increased by less than 15%, decreased by 15% or below, and decreased by more than 15%. Linear regression analysis was used to investigate the relationship between GNI per capita and age-standardized DALY rates of CVD and its 11 subcategories. A joinpoint regression analysis was used to access significant trends in CIs from 2000 to 2019 by estimating annual percentage change (APC) and average annual percentage change (AAPC). The APC for the segments and the AAPC is a summary measure of the trend, which expresses the weighted average of APC for the overall period , 22. TheAll statistical analyses were conducted using SPSS v25.0  and Stata v13.0 . All joinpoint analyses were performed using Joinpoint Statistical Software .The change rate of age-standardized DALY rates for CVD from 2000 to 2019 across 186 countries is presented in Fig. Among all subcategories of CVD, more than half of countries had a decrease in age-standardized DALY rates from 2000 to 2019 apart from atrial fibrillation and flutter and peripheral artery disease, ranging from 57% for endocarditis to 98% for rheumatic heart disease  in 2000 to − 0.151 (95% CI: −0.190 to − 0.112) in 2019  in 2000 to 0.159 (95% CI: 0.116 to 0.201) in 2019 for non-rheumatic valvular heart disease, from 0.081 (95% CI: 0.059 to 0.104) in 2000 to 0.065 (95% CI: 0.043 to 0.086) in 2019 for atrial fibrillation and flutter, from 0.147 (95% CI: 0.105 to 0.190) in 2000 to 0.092 (95% CI: 0.051 to 0.134) in 2019 for aortic aneurysm, and from 0.170 (95% CI: 0.113 to 0.227) in 2000 to 0.128 (95% CI: 0.066 to 0.189) in 2019 for peripheral artery disease  in 2000 to − 0.301 (95% CI: −0.391 to − 0.210) in 2019 for rheumatic heart disease, from − 0.035 (95% CI: −0.085 to 0.015) in 2000 to − 0.119 (95% CI: −0.172 to − 0.066) in 2019 for ischemic heart disease, from − 0.154 (95% CI: −0.191 to − 0.116) in 2000 to − 0.206 (95% CI: −0.247 to − 0.166) in 2019 for stroke, from − 0.218 (95% CI: −0.273 to − 0.164) in 2000 to − 0.229 (95% CI: −0.281 to − 0.177) in 2019 for hypertensive heart disease, and from − 0.133 (95% CI: −0.180 to − 0.086) in 2000 to − 0.173 (95% CI: −0.214 to − 0.131) in 2019 for other cardiovascular and circulatory diseases. In addition, the concentration curves of age-standardized DALY rates for endocarditis lay above the equality line, and the distances above the equality line grew increasingly close, with the CI increasing from − 0.077 (95% CI: −0.131 to − 0.022) in 2000 to − 0.067 (95% CI: −0.124 to − 0.009) in 2019  from 2000 to 2019. The negative CIs demonstrated that the pro-poor inequality distribution of the overall burden of CVD was increasing over time. The decline in the CIs of age-standardized DALY rates of CVD changed over time in four phases: accelerating from 2000 to 2006 (− 3.8%), further accelerating from 2006 to 2011 (− 4.4%), and decelerating from 2011 to 2014 (− 2.1%) and 2014–2019 (− 0.1%), where the first three periods were with significant reductions  for ischemic heart disease to − 0.2% (95% CI: −0.4 to − 0.1) for hypertensive heart disease  has resulted in a dearth of significant policy support and concern for CVDs since 2000 . Many loIt is encouraging that, in the current study, inequalities in the global burden of non-rheumatic valvular disease, atrial fibrillation and flutter, aortic aneurysm, peripheral artery disease, and endocarditis have progressively decreased over the past 20 years. Although the exact reasons for these positive trends are uncertain, of importance is the recognition of the diseases as a major public health problem in countries worldwide, and considerable resources have been invested in the surveillance and control of the risk factors, such as smoking cessation and hypertension management. In developed countries and regions, effective low-cost screening, advanced treatment technology, and better health awareness reduced the prevalence and mortality of these CVDs –42, as eOur analysis of the temporal trends in inequalities of CVD burden and its subcategories is beneficial for illustrating global patterns of CVD inequality, which may contribute to policy and strategy development worldwide. Further expansion of health assistance and universal health coverage for lower socioeconomic development countries, such as much of the sub-Saharan and Pacific Island nations, is warranted as a priority to curb the progress of CVD inequities. The need for early detection and treatment cannot be overemphasized, especially for regions with restricted health resources. Given that most CVDs can be prevented by targeting behavioral risk factors, future research on the control strategies of CVD is needed to continuously focus on the intervention and evaluation of these factors, which must be improved. One open-label, cluster-randomized trial  demonstrated that salt substitution could lower the risk of cardiovascular events , and a mOne strength of the current study is the comprehensive population-based assessment and comparison of trends in socioeconomic inequalities of CVD burden worldwide based on nations’ income levels, which adds to the evidence base for the development and implementation of strategies on the control and management of CVD and its subcategories. However, there are several limitations to be considered. First, as with other GBD studies, the accuracy of estimates in our study depends on the quality and quantity of data sources, and these are associated with detection techniques, incomplete case-reports, and data collection and encoding methods used in different countries. Second, the absence of epidemiological surveys in certain regions may result in a hidden incidence, particularly in low-income countries, which means that inequality of CVD burden is potentially underestimated. Third, it’s important to acknowledge that the joinpoint regression method used for the secondary data analysis did not take into account the uncertainty measure in the GBD raw data. This omission might lead to an underestimation of the uncertainty associated with the trends in AAPC. Fourth, caution is warranted when interpreting the results, as correlated evidence from other studies does not necessarily indicate causality. Furthermore, the policy implications presented in the study may not be universally applicable to countries with distinct socio-economic and healthcare contexts. Fifth, this study is a secondary analysis grounded in GBD epidemiologic data, inherently lacks the control of potential confounders, including the influence of epidemiological transition across countries. Finally, our study is cross-national, which may introduce bias due to a lack of understanding of the disparities that exist between regions within countries.Globally, the burden of CVD has decreased in 170 (91%) of 186 countries over the past 20 years. Low-income countries continue to share the major burden of CVD. Socioeconomic inequality in global burden of CVD has worsened over this period, and inequalities vary across the different subcategories. Effective and cost-effective interventions for preventing CVD are crucial in curbing the progress of CVD inequalities.Additional file 1: Fig S1. The change rate of age-standardized DALY rates for rheumatic heart disease from 2000 to 2019. Fig S2. The change rate of age-standardized DALY rates for ischemic heart disease from 2000 to 2019. Fig S3. The change rate of age-standardized DALY rates for stroke from 2000 to 2019. Fig S4. The change rate of age-standardized DALY rates for hypertensive heart disease from 2000 to 2019. Fig S5. The change rate of age-standardized DALY rates for non-rheumatic valvular heart disease from 2000 to 2019. Fig S6. The change rate of age-standardized DALY rates for cardiomyopathy and myocarditis from 2000 to 2019. Fig S7. The change rate of age-standardized DALY rates for atrial fibrillation and flutter from 2000 to 2019. Fig S8. The change rate of age-standardized DALY rates for aortic aneurysm from 2000 to 2019. Fig S9. The change rate of age-standardized DALY rates for peripheral artery disease from 2000 to 2019. Fig S10. The change rate of age-standardized DALY rates for endocarditis from 2000 to 2019. Fig S11. The change rate of age-standardized DALY rates for other cardiovascular and circulatory diseases from 2000 to 2019. Figs S12-29. Concentration curves of age-standardized DALY rates for CVD from 2001 to 2018. Figs S30-47. Concentration curves of age-standardized DALY rates for rheumatic heart disease from 2001 to 2018. Figs S48-65. Concentration curves of age-standardized DALY rates for ischemic heart disease from 2001 to 2018. Figs S66-83. Concentration curves of age-standardized DALY rates for stroke from 2001 to 2018. Figs S84-101. Concentration curves of age-standardized DALY rates for hypertensive heart disease from 2001 to 2018. Figs S102-119. Concentration curves of age-standardized DALY rates for non-rheumatic valvular heart disease from 2001 to 2018. Figs S120-137. Concentration curves of age-standardized DALY rates for cardiomyopathy and myocarditis from 2001 to 2018. Figs S138-155. Concentration curves of age-standardized DALY rates for atrial fibrillation and flutter from 2001 to 2018. Figs S156-173. Concentration curves of age-standardized DALY rates for aortic aneurysm from 2001 to 2018. Figs S174-191. Concentration curves of age-standardized DALY rates for peripheral artery disease from 2001 to 2018. Figs S192-209. Concentration curves of age-standardized DALY rates for endocarditis from 2001 to 2018. Figs S210-227. Concentration curves of age-standardized DALY rates for other cardiovascular and circulatory diseases from 2001 to 2018. Table S1. Trends in concentration index of age-standardized DALY rates for rheumatic heart disease from 2000-2019. Table S2. Trends in concentration index of age-standardized DALY rates for ischemic heart disease from 2000-2019. Table S3. Trends in concentration index of age-standardized DALY rates for stroke from 2000-2019. Table S4. Trends in concentration index of age-standardized DALY rates for hypertensive heart disease from 2000-2019. Table S5. Trends in concentration index of age-standardized DALY rates for non-rheumatic valvular heart disease from 2000-2019. Table S6. Trends in concentration index of age-standardized DALY rates for cardiomyopathy and myocarditis from 2000-2019. Table S7. Trends in concentration index of age-standardized DALY rates for atrial fibrillation and flutter from 2000-2019. Table S8. Trends in concentration index of age-standardized DALY rates for aortic aneurysm from 2000-2019. Table S9. Trends in concentration index of age-standardized DALY rates for peripheral artery disease from 2000-2019. Table S10. Trends in concentration index of age-standardized DALY rates for endocarditis from 2000-2019. Table S11. Trends in concentration index of age-standardized DALY rates for other cardiovascular and circulatory diseases from 2000-2019.